Delivery Hero's In-House Analytics Handles 10x Load

Delivery Hero built an in-house user tracking platform that handles 10 times more load than Google Analytics while capturing 97% of data.

Delivery Hero has successfully replaced Google Analytics with a custom-built, in-house user tracking platform. In a presentation at InfoQ, engineer Alina Krasavina detailed how the company migrated away from the popular third-party service. The new system was designed with a simplistic yet highly scalable architecture, allowing it to handle ten times more load than its predecessor. Despite the increased traffic, the platform successfully captures 97% of all tracking data. This strategic shift was motivated by the need for greater control over data, improved cost-effectiveness at scale, and the ability to better manage user privacy, which are common challenges for companies relying on external analytics tools.

This case study offers a practical blueprint for CTOs, developers, and founders who feel limited by off-the-shelf analytics solutions. It demonstrates a viable path toward data sovereignty, where a company owns and controls its data infrastructure completely. Building an in-house system allows for customized data collection tailored to specific business needs, eliminates vendor lock-in, and can lead to significant cost savings. For security and IT teams, it provides direct oversight of user data, which simplifies compliance with privacy regulations like GDPR. Delivery Hero's success with a simple design challenges the idea that such systems must be overly complex to be effective.

The move reflects a broader industry trend where companies are investing in first-party data infrastructure to gain a competitive edge and address growing privacy concerns. As users and regulators demand more transparency, having full ownership of the analytics stack is becoming a key strategic advantage. While building a custom solution requires a significant engineering investment, Delivery Hero's experience shows that it can be a powerful and efficient alternative to relying on third-party vendors for core business functions. It provides a compelling example for any organization weighing the classic “build versus buy” decision for its data analytics.